Using multiplayer simulation to evaluate incentive-based strategies for reducing congestion and carbon emissions: A case study of campus travel in Huainan, China

Abstract

The debate between price-based and quantity-based approaches to carbon regulation has been a longstanding focus among scholars aiming to control emissions and advance low-carbon development. This study explores and compares the effects of two intervention models on individuals’ low-carbon travel decisions. Using Huainan in China as a case study of urban campus travel, we employed a multiplayer online simulation experiment conducted on the ZTree platform. The experimental scenario simulated short-distance leisure travel (within 5 km) under three conditions: no intervention, price-based intervention (via a travel-mode tax), and group-targeted intervention (via a shared carbon emission quota). A total of 600 campus travelers were organized into 20 groups of 30 participants. Across 30 rounds of decision-making, participants selected transport modes under varying policy scenarios. The findings revealed that both interventions effectively reduced traffic congestion and carbon emissions, with the group target-based intervention demonstrating more consistent and robust effects. Additionally, both psychological factors (attitude, subjective norms, perceived behavioral control, intention, face consciousness, and habits) and demographic factors (gender, age, income, family structure, private car ownership, and housing status) predict changes in participants’ traffic-related carbon emissions before and after the experiment, with demographic factors exerting a stronger influence overall. This study offers empirical evidence to inform policy design aimed at reducing urban traffic congestion and transport-related carbon emissions, particularly within university commuting contexts in medium-sized Chinese cities.